1. Field of the Invention
The present disclosure relates to methods and systems for detection and analysis of seismic waveform data and, more particularly, methods and systems for the real-time 3D detection of microseismic events usable in non-vertical boreholes.
2. Background of the Related Art
A reliable real-time scheme for the detection/localization of microseismic events is important for Hydraulic Fracture Monitoring (HFM) and Reservoir Monitoring (RM), to enable timely decision making during stimulation operations and for reservoir management, among other applications. Microseismic event detection, location estimation and source parameter analysis provide critical information on the position, extent and growth of fractures caused during fluid injection or through other active or passive causes.
Standard historical approaches to determining event detection and location, such as the Geiger algorithm, have relied on time-picking of P-wave and S-wave arrival phases together with the computation of P-wave polarization. These approaches have been applied to individual sensor channels and hence have tended to be unstable and unreliable in the presence of significant noise. Accordingly, intervention, pick verification, and manual pick adjustment have often been required, but those steps are time consuming and do not permit real-time operation.
A model-based algorithm to image the distribution of microseismic sources in both time and space has been described and applied to episodic tremors in a subduction zone. An efficient implementation of a similar method was developed for HFM and is disclosed in U.S. Pat. No. 7,391,675, which is hereby incorporated in its entirety. In particular, the '675 reference discloses what has been referred to as the Coalescence Microseismic Mapping (CMM) technique for real-time event detection and localization of seismic events. This approach does not require that discrete time picks be made on each of the waveforms. Rather, individual streams of seismic multi-component waveforms at each sensor are operated upon continuously using a function former, such as a signal-to-noise ratio (SNR) estimator. The output of the function formers from all sensors are then individually mapped (migrated) into a 4D space-time map of hypocenter and origin time using model based travel times. This allows simultaneous event detection and localization by identifying map locations and origin times for which a collective or multi-sensor processor output exceeds a detection threshold. Waveform polarization is included in the discrimination algorithm both as a means of distinguishing consistent portions of the waveform streams which contain P-wave and S-wave phases that are being mapped, and as means of orienting a vertical (radial-depth) plane of localization, as the mapping algorithm is run in a 2D rather than 3D spatial geometry. In other words, the CMM algorithm delivers in real-time, event detection and location based on a signal to noise ratio onset measure using modeled travel times of P and S-waves.
Moreover, it should be noted that the CMM algorithm is a robust event-detector in the presence of noise, and that it is very efficient in making an event detection and location by assuming a vertical receiver array or, more precisely, by assuming that the seismic signal receivers disposed in the wellbore are positioned in the plane containing the center of a target grid. However, the CMM technique as described above has some inherent drawbacks, including that it has not provided standard measures of localization uncertainty which in the past have been computed from the measurement versus model residuals associated with discrete time picks, and that the assumed geometries cause the accuracy of the above approach to break down as wellbore deviation increases and events move away from the target plane. The CMM technique may also encounter drawbacks for multi-well data acquisition, where the ability to handle an arbitrarily distributed receiver network is preferred.
Accordingly, it will be appreciated that there exists a desire to improve upon conventional seismic waveform data processing in order to improve the accuracy and efficiency of seismic measurements.
The limitations of conventional waveform data processing techniques noted in the preceding are not intended to be exhaustive but rather are among many which may reduce the effectiveness of previously known techniques. The above should be sufficient, however, to demonstrate that methods and systems for acquiring and processing seismic data existing in the past will admit to worthwhile improvement.